A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization

In this paper we present our work on improving the efficiency of adversarial training for unsupervised video summarization. Our starting point is the SUM-GAN model, which creates a representative summary based on the intuition that such a summary should make it possible to reconstruct a video that is indistinguishable from the original one. We build on a publicly available implementation of a variation of this model, that includes a linear compression layer to reduce the number of learned parameters and applies an incremental approach for training the different components of the architecture. After assessing the impact of these changes to the model’s performance, we propose a stepwise, label-based learning process to improve the training efficiency of the adversarial part of the model. Before evaluating our model’s efficiency, we perform a thorough study with respect to the used evaluation protocols and we examine the possible performance on two benchmarking datasets, namely SumMe and TVSum. Experimental evaluations and comparisons with the state of the art highlight the competitiveness of the proposed method. An ablation study indicates the benefit of each applied change on the model’s performance, and points out the advantageous role of the introduced stepwise, label-based training strategy on the learning efficiency of the adversarial part of the architecture

Beernet: RMI-free peer-to-peer networks

The key issue in distributed programming is partial failure: how to handle failures of part of the system. This unavoidable property causes uncertainty because we cannot know whether a remote object is ever going to reply to a message. It is also the reason why RMI/RPC is difficult to use. In this paper we describe the most convenient object-oriented mechanism we have found to develop peer-to-peer applications effectively, namely by using active objects that communicate via asynchronous message passing and fault streams for failure handling. We show that this works better than the usual approach of using RMI to communicate and distributed exceptions for failure handling. We define our peers as lightweight actors and we use them to build a highly dynamic peer-to-peer network that deals well with partial failure and non-transitive connectivity. We give many code examples to show the simplicity and naturalness of our approach

Quality of information accompanying on-line marketing of home diagnostic tests

Objective To assess the quality of information provided to consumers by websites marketing medical home diagnostic tests

Report of the 1st International Workshop "Conservation and research networking on short‐beaked common dolphin (Delphinus delphis) in the Mediterranean Sea"

Oceanomare Delphis Onlus (Italy), BICREF (Malta) and OceanCare (Switzerland), jointly organized the 1st International Workshop on short-beaked common dolphin (Delphinus delphis, Linneaus 1758), which took place in Ischia, between the 13th and the 15th of April, in order to assess the status of the Mediterranean population, understand the major threats it faces and outline conversation action plans. Furthermore, the process for the IUCN red List re-assessment of the Mediterranean population was started. The workshop brought together representatives of leading academic institutions, NGOs and research groups of the Mediterranean and European countries with contributions from Algeria, France, Greece, Ireland, Israel, Italy, Libya, Malta, United Kingdom, Slovenia, Spain, Switzerland, Tunisia and Turkey.peer-reviewe